code: def adjust_screening_percentile(
      screening_percentile, mask_img, verbose=0
  ):
      """Adjust the screening percentile according to the MNI152 template.
  
      Parameters
      ----------
      screening_percentile : float in the interval [0, 100]
          Percentile value for ANOVA univariate feature selection. A value of
          100 means 'keep all features'. This percentile is expressed
          w.r.t the volume of a standard (MNI152) brain, and so is corrected
          at runtime by premultiplying it with the ratio of the volume of the
          mask of the data and volume of a standard brain.
  
      mask_img : nibabel image object
          Input image whose voxel dimensions are to be computed.
  
      verbose : int, default=0
          Verbosity level.
  
      Returns
      -------
      screening_percentile: float in the interval [0, 100]
          Percentile value for ANOVA univariate feature selection.
  
      """
      original_screening_percentile = screening_percentile
      # correct screening_percentile according to the volume of the data mask
      mask_volume = get_mask_volume(mask_img)
      if mask_volume > 1.1 * MNI152_BRAIN_VOLUME:
          warnings.warn(
              "Brain mask is bigger than the volume of a standard "
              "human brain. This object is probably not tuned to "
              "be used on such data.",
              stacklevel=2,
          )
      elif mask_volume < 0.005 * MNI152_BRAIN_VOLUME:
          warnings.warn(
              "Brain mask is smaller than .5% of the volume "
              "human brain. This object is probably not tuned to "
              "be used on such data.",
              stacklevel=2,
          )
  
      if screening_percentile < 100.0:
          screening_percentile = screening_percentile * (
              MNI152_BRAIN_VOLUME / mask_volume
          )
          screening_percentile = min(screening_percentile, 100.0)
      # if screening_percentile is 100, we don't do anything
  
      if verbose > 1:
          print(
              f"Mask volume = {mask_volume:g}mm^3 = {mask_volume / 1000.0:g}cm^3"
          )
          print(
              "Standard brain volume "
              f"= {MNI152_BRAIN_VOLUME:g}mm^3 "
              f"= {MNI152_BRAIN_VOLUME / 1.0e3:g}cm^3"
          )
          print(
              f"Original screening-percentile: {original_screening_percentile:g}"
          )
          print(
              f"Volume-corrected screening-percentile: {screening_percentile:g}"
          )
      return screening_percentile
  
file_path: nilearn/nilearn/_utils/param_validation.py
imports: import numbers
  import warnings
  import numpy
  from sklearn.feature_selection import SelectPercentile, f_classif, f_regression
  from .niimg import _get_data
label_desc: Use image node_id 3 for calling a global function with example usage: adjust_screening_percentile(screening_percentile, mask_img, verbose) and returns: screening_percentile